Understanding precision, recall, and F1-score. Interplay of precision and recall. Choosing the right metrics for your task. Handling imbalanced data. Evaluate using metrics beyond accuracy, especially precision, recall, and F1-score, which give an insight into model performance for each class. Resolve class imbalances using methods like oversampling, undersampling, or cost-sensitive learning. Proper handling is crucial for reliable model operation